Towards the next generation of machine learning models in additive manufacturing: A review of process dependent material evolution
Keywords
1. Introduction
1.1. Additive manufacturing
Fig. 1. AM processes in machine learning.
1.2. Research strategy
Fig. 2. Overall structure of this review paper.
Fig. 3. Methodology used to conduct this literature review.
Fig. 4. Categorizing the articles reviewed in this paper using (a) PCA, (b) t-SNE.
Fig. 5. Co-current network diagram for three identified clusters, namely (a) the papers employed experimental and physics-based approaches, (b) the papers employed data-driven approaches, and (c) review papers.
1.3. Defect detection
1.4. Appliction of machine learning in additive manufactuiring
2. Types of data in AM
Fig. 6. Word cloud of the input parameters studied in the articles reviewed.
Fig. 7. Relationship between manufacturing processing parameters, thermal history, solidification, microstructure, and mechanical behavior of AM parts.
2.1. Porosity formation
Fig. 8. Charachteristic pores in SLM (a) entrapped gas porosity, (b) incomplete melting-induced porosity, (c) lack of fusion with unmelted particles inside large irregular pores, and (d) cracks [104].
Fig. 9. Schematics of the relationships between surface morphology and defect formation in a powder-bed fusion type AM process [27].
Fig. 10. (a) Morphology of melt tracks present on the top surface of the sample built, (b) L-PBF process map of pure copper, indicating a threshold line between the conduction and keyhole mode melting regimes [97].
2.2. Dimensional inaccuracies
Fig. 11. (a) Surface microstructure of two different sizes of powders, (b) Optical microscopy of powder (left) and (right), and (c) sintered density and volumetric shrinkage relationship of powder and its bimodal mixtures [121].
2.3. Melt pool dimensions
2.4. Remelted depth
2.5. Thermal history
Fig. 12. Geometric error compensation of Ti-6Al-4 V in the L-PBF process. The input parameters are (a) the thermal history and (b) processing parameters. The output is (c) the predicted distortion using an ML model [163].
2.6. Acoustic and spectra signals
3. Machine learning
Fig. 13. Applications of machine learning in the AM field.
Fig. 14. Online learning flowchart.
Table 1. Machine learning models.
| Model | Description | Application in AM | Feature |
|---|---|---|---|
| Random forest (RF)[171] | Consisting of several decision trees trained on random sub-samples. | Reg., Clas. | |
| Returning the average outputs of the decision trees (regression). | |||
| Returning the highest vote of the decision trees (classification). | |||
| Support vector machine (SVM) [172] | Predicting based on finding hyperplane, separating data points of different categories (regression). | Reg., Clas. | |
| Gaussian process regression model (GPR) [173] | Probabilistic algorithms, returning a probability distribution over output values. | Reg., Clas. | Predict fairly well on small datasets. |
| Ridge linear regression (Ridge) [174] | A regularized linear regression. | Reg. | Reduce the model complexity and overfitting. |
| Artificial neural network (ANN)[175] | Mimics human brain with individual networks exchanging information. | Reg., Clas. | Large dataset required. |
| Perform better with more than 1 hidden layer. | |||
| Includes an input layer, hidden layer(s), and an output layer. | |||
| Convolution neural network (CNN) [176] | Consisting of multiple convolutional kernels to compute feature maps. | Clas. | Widely used for image recognition in AM. |
| XGBoost [177] | Implements gradient boosted trees to generate predictions. | Reg., Clas. | |
| Logistic regression (LR) [178] | Probability values and logistic function outputs are used in classification. | Reg., Clas. | |
| Gradient Boosting trees (GB) [179] | It builds one tree at a time, which tries to improve errors produced by the previous tree. | Reg., Clas. | |
| Lasso linear regression (Lasso) [180] | A regularization method, optimizing the weights of linear regression. | Reg. | More accurate prediction with less overfitting compared to linear regression. |
| K-neighbours regression (KNN/KNR) [181] | A nonlinear regression algorithm. | Reg. | The larger k, the more accurate interpolation, and the higher computational cost. |
| Dense convolutional networks (DenseNet)[182] | Connecting each layer to every other layer in a feed-forward fashion. | Clas. | Effective for handling overfitting, and huge computation. |
| Suitable for classifying melt pool images. | |||
| Self-organizing map (SOM)[183] | An artificial neural networks-based model that employs competitive learning to discern patterns. | Clus. | Perform well in unsupervised tasks. |
| Highly capable of visualizing data. | |||
| Self-organized and error driven (SOEDNN) [184] | A hybridized SOM and ANN method. | Clas. | Improved version of ANNs. |
| Principal component analysis (PCA) [185] | A statistical technique, converting a set of correlated variables into a set of values linearly uncorrelated. | Dim. reduction | In AM field, help to visualize data. |
| Multilinear principal component analysis (MPCA) [186] | A method to extract features of multidimensional data expressed as a tensor. | Dim. reduction | Tensor based algorithm |
| Linear discriminant analysis (LDA) [187] | A method to find a linear combination of features, which characterizes or separates two or more classes. | Dim. reduction | LDA is closely related to PCA. |
| Bayesian network classifier (BN) [188] | A probabilistic classifier, providing probability information about the examined product. | Clas. | Useful in defect detection. |
| Spectral convolutional neural networks (SCNN) [189] | A recent extension of CNNs with improved efficiency in classification/regression tasks. | Reg, Clas. | Operating well on irregular data grids. |
| Support vector regression (SVR) [190] | A method to find the best fit line. | Reg. | Uses the same principles as SVM. |
| Locally linear embedding (LLE) [191] | It computes low dimensional neighborhood preserving embeddings of high dimensional data. | Clus. | Discover nonlinear structure in high dimensional data |
| Bag-of -Words (BoW) [192] | It works based on quantization of affine invariant descriptors of image patches. | Clus. | Simple computationally efficient and intrinsically invariant method |
| K-means clustering algorithm (KMC) [193] | A simple method to classify dataset using a pre-defined number of clusters (k). | Clus. | Widely used image and acoustic emission analysis. |
| Deep belief network (DBN) [194] | A probabilistic graphical model that contains numerous layers for deep learning. | Clus. | Layer-by-layer training repeated to learn a deep model. |
| Gaussian mixture model (GMM) [195] | A function comprised of several Gaussians. | Clas. | Relatively time efficient model. |
| Deep reinforcement learning (DRL) [167] | A combination of deep learning and reinforcement learning. | Clas., Reg. | Automatically learning a policy with minimal manual training. |
| Proximal policy optimization (PPO) [196] | A widely successful actor-critic method. | Clas., Reg. | Simple to use and tune. |
| Deep Q-network (DQN) [197] | A combination of deep learning and reinforcement learning. | Clas., Reg. | Fast convergence. |
| Soft actor critic (SAC) [198] | An off-policy actor-critic method. | Clas., Reg. | One of the most efficient RL algorithms used in real-world robotics. |
3.1. Supervised learning
Fig. 15. Roadmap of a data-driven approach to analyze fatigue performance of AM parts using miniature specimens [199].
3.1.1. Regression
3.1.1.1. Conventional models
Fig. 16. Schematic description of workflow applied in a study by Ye et al. [208].
Fig. 17. Schematic description of workflow applied in a study by Akbari et al. [101].
Fig. 18. Classification decision boundaries of the dataset based on power and velocity for (a) random forest model, (b) logistic regression model, (c) Neural Network model. Confusion matrix for the classification task for random forest classifier with baseline plus absorptivity coefficient (d) 1, (e) 2, (f) with baseline plus both absorptivity coefficients and hatch spacing as features in a study by Akbari et al. [101].
3.1.1.2. Physics-informed models
Fig. 19. Workflow of surrogate model construction [214].
Fig. 20. Control performance for a six-track with length under the laser scan speed of [217].
Fig. 21. Comparison of the predictions of the temperature and melt pool fluid dynamics of FEM, physics-informed neural network, and experiment (laser power of and scanning speed of at quasi-steady state. [218].
Fig. 22. Applying RNN-DNN model to predict thermal history [221].
3.1.2. Classification
3.1.2.1. Conventional models
Fig. 23. (a) A general overview of parameter selection using cross-validation (b) the part with an unhealthy layer, which includes pores, and (c) one iteration of cross-validation in a study by Seifi et al. [223].
Fig. 24. Developing ANN model using acoustic emissions in a study by Gaja et al. [224].
Fig. 25. Flowchart of the steps taken for discontinuity detection in a study by Gobert et al. [228].
Fig. 26. Demonstration of porosity prediction procedure using supervised ML [16].
Table 2. The relationship between the model, data, and accuracy.
| Reference | Model | Data size | Accuracy |
|---|---|---|---|
| Ren et al.[221] | DNN | 47152 | |
| CNN | |||
| RNN | |||
| DNN-RNN | |||
| Ren et al.[162] | RNN | 340 | 95.05% |
| Khanzadeh et al. [62] | SOM | 1564 | 96 % |
| Khanzadeh et al.[16] | QDA | 2800 | |
| LDA | |||
| DT | |||
| SVM | |||
| KNN | |||
| Smoqi et al.[229] | KNN | 22400 | |
| SVM | |||
| LR | |||
| CNN | |||
| Du et al.[240] | Genetic algorithm | 166 | 90% |
| Pham et al.[212] | FFNN | million | 99% |
| Fetni et al.[213] | ANN | 4 million | 99% |
| Roy et al.[214] | ANN | 26000 (training) | Error < 5% |
| Ren et al.[217] | GPR | mm2 | |
| mm2 | |||
| Yang et al.[231] | CNN | 5689 | 91% |
| Kwon et al.[235] | ANN | 13200 | Error = 1.1% |
| Akbari et al.[101] | RF | 2200 | |
| XGBoost | , MAE | ||
| NN | , MAE | ||
| GPR, SVM, LR, GB, Lasso, Ridge | |||
| Lasso | Average Rank = 7 | ||
| Rdige | Average Rank = 8 | ||
| ElasticNet | Average Rank = 7 | ||
| SVR | Average Rank = 3.7 | ||
| KNR | Average Rank = 6 | ||
| ANN | Average Rank = 2 | ||
| Yuan et al.[230] | DenseNet | 80 | 99.3% |
| Gaja et al.[224] | LR | 37 | MSE |
| ANN | MSE | ||
| Ye et al.[227] | SVM | 34 | |
| Hertlein et al.[236] | BN | 349 | - |
| Liu et al.[237] | LR, GPR, SVR | 549 | Error = 10–26% |
| Ren et al.[247] | KMC, LSTM-Autoencoder | 4290 | = |
| Ye et al.[248] | DBN | ||
| Ye et al.[249] | DBN | - | |
| Okaro et al.[250] | GMM | 49 | |
| Yuan et al.[251] | SLM | 1000 | Classification: |
| Regression: |
Fig. 27. Schematic representation of data processing and machine learning approach used in Smoqi et al. [229] work.
Fig. 28. The proposed in-situ monitoring method by Yuan et al. [230].
Fig. 29. Structure of CNN code employed in a study by Khan et al. [232].
3.1.2.2. Physics-informed Models
Fig. 30. Summary of developing ML and PIML models in a study by Liu et al. [237] work.
Fig. 31. Schematic representation of the approach proposed and used in Du et al. [240] work.
Table 3. Supervised learning.
| Reference | AM process | Materials | Input | Output | ML method |
|---|---|---|---|---|---|
| Rong-Ji et al. [119] | SLS | - | , BP, , | Material shrinkage | ANNs |
| Tran et al. [252] | SLM | SS316L | BP, v, LSS, , PM, PSD | Depth of melt pool | ANNs |
| Lee et al. [122] | SLM | alloy 625, 718 | BP, v, LSS, , PSD | MP dimensions | Br, Kr, LR, NN, RF SVM |
| Schmid et al. [253] | SLM | AlSi10Mg | BP, | MPdimensions | CNNs |
| Chen et al. [254] | SLM | TiB2, AlSi10Mg | BP, v | MP dimensions | ANNs |
| Meng and Zhang [255] | SLM | SS316L | BP, v | Remelted depth | GP |
| Tapia [100] | SLM | SS17-4 PH | BP, v | Porosity, | GP |
| Olleak and Xi [59] | SLM | Ti-6Al- 4v Alloy | BP, , LSS | MP | |
| dimensions | GP, FEM | ||||
| Kamath [29] | SLM | SS316L | BP, , LSS | Depth of MP | GP, MT |
| Mozaffar [64] | DED | SS316L | BP, , GS, S | Thermal history | RNN |
| Zhang [58] | DED | Nickel-718 | BP, | Thermal history | xGBoost, LSTM |
| Singh et al. [256] | SLM | Bronze | BP, | MLP | |
| Tapia et al. [257] | SLM | SS316L | BP, v | Depth of MP | GP |
| Caiazzo and Caggiano [258] | DED | 2020 Al alloy | BP, v, PFR | MP dimensions | ANNs |
| Lu et al. [259] | DED | SS316 | BP, v PFR | ANNs, LS-SVM | |
| Aoyagi et al. [27] | EBM | COCr | BP, v | Porosity | SVM |
| Khanzadeh et al. [16] | DED | Ti-6Al-4 V | Thermal history | Porosity | DT, KNN, SVM, DA |
| Jafari-Mardani et al. [260] | DED | Ti-6Al-4 V | Thermal history | Porosity | KNN, NN, SOEDNN |
| Kappes et al. [261] | SLM | Inconel 718 | PP, PO, FRP | Porosity | RFN |
| Ren et al. [221] | DED | - | TS, , geometry shape | Thermal history | RNN-DNN |
| Ren et al. [162] | DED | SS316L | TS | Thermal history | RNN |
| Smoqi et al. [229] | SLM | ATI 718Plus | MP dimensions, T | Porosity | KNN, SVM, LR, CNN |
| Pham et al. [212] | DED | M4 high-speed steel | BP, t | Thermal history | FFNN |
| Fetni et al. [213] | DED | 316L SS | Thermal history | FFNN | |
| Roy et al. [214] | FFF | Thermal history | ANN | ||
| Ren et al.[217] | SLM | Inconel 625 | BP, Thermal history | MP dimensions | GPR |
| Zhu et al.[218] | - | Inconel 625 | BP, v | MP dimensions, Thermal history | NN |
| Yang et al.[231] | SLM | Inconel 625 | TS | MP dimensins | CNN |
| Kwon et al.[235] | SLM | SUS316L | MP images | ANN | |
| Ye et al.[208] | DED | SS316 | BP, v, PFR, W, T | MP depth and height | ANN |
| Yuan et al.[230] | DED | SS316 | Melt pool shapes | MP state detection | DenseNet |
| Mahmoudi et al.[222] | LPBF | SS | Thermal history | Anamoly detection | GP |
| Gaja et al.[224] | LMD | Ti-6Al-4 V | Acoustic emissions | Defect detection | LR, ANN |
| Gobert et al.[228] | SLM | SS GP-1 | Layerwise optical images | Defect detection | SVM |
| Seifi et al.[223] | DED | Ti-6AL-4 V | Thermal history | Anamoly detection | SVM |
| Ye et al.[227] | SLM | SS 304L | Acoustic emissions | Defect detection | SVM |
| Hertlein et al.[236] | SLM | 316L | , hardness, roughness, ultimate tensile strength | BN | |
| Shevchik et al.[225] | SLM | SS 316L | Acoustic emissions | Anamoly detection | CNNs, SCNNs |
| Liu et al.[237] | SLM | Inconel 718 | LED, LR, PI | Porosity | LR, GPR, SVR |
Fig. 32. Framework for achieving high-performance parts using online PIML methods suggest by Yan et al. [220].
3.2. Unsupervised learning
Table 4. Unsupervised learning.
| Reference | AM process | Materials | Input | Output | ML method |
|---|---|---|---|---|---|
| Donegan et al. [285] | SLM | Ti-6Al-4 | Thermal history | Process history | KMC |
| Tan et al. [286] | SLS | - | IR images | Anamoly detection | EDS |
| Scime and Beuth [49] | SLM | Inconel 718 | IR images | Anamoly detection | KMC |
| Scieme and Beuth [279] | SLM | - | powder bed images | Anamoly detection | KMC |
| Wu et al. [200] | - | - | Optical image | Anamoly detection | Not specified |
| Pagan et al. [287] | - | IN625 | Diffraction images | Anamoly detection | LLE |
| Grasso et al. [273] | SLM | AISI | Optical images | Defect detection | PCA |
| Khanzadeh et al. [62] | DED | Ti-6Al-4 V | Thermal history | Porosity | SOM |
| Scime and Beuth [49] | DED | Inconel 718 | Thermal history | Porosity and balling | BoW |
| Senll et al. [272] | SLM | Ti-6Al-4 V, Inconel 718, Haynes 282 | 2D and 3D pores | Porosity type | KMC |
| Taheri et al. [265] | DED | Ti-6Al-4 V | AE | Process conditions | KMC |
| Gaja et al. [263] | DED | Ti-6Al-4 V and H13 tool steel | AE | Crack, porosity | KMC |
| Ren et al.[247] | DED | Al7075 alloy | BP, v, PFR | Porosity | KMC |
| Ye et al.[248] | SLM | SS 304 | AE | Defect pattern | DBN |
| Ye et al.[249] | SLM | SS 304 | NIR image | Defect pattern | DBN |
| Gracia-Moreno et al.[274] | DED | Al-5083 | Optical microscope images | Defect pattern | SOM |
3.2.1. Clustering
Fig. 33. Step-by-step operations used to perform acoustic emission analysis in a study by Gaja and Liou [263].
Fig. 34. A sample SOM network.
Fig. 35. Predicting pores from thermal profiles [62].
Fig. 36. Developing a hybrid supervised and unsupervised model to classify melt pools [49]. (a)-(f) feature extraction process using CV and BoW (g) fingerprints representation used to describe melt pool morphologies, (h)-(i) linking in-situ and ex-situ results (the microgrpahs shown in step (i) were collected from [96], and (j)-(k) training of the classification algorithm and classifying new data.
3.3. Semi-supervised learning
Fig. 37. Receiver operating characteristic curves for the ensemble of experiments in a study conducted by Okaro et al. [250]. The red line indicates the benchmark result (supervised learning when all 49 points are labeled).
Table 5. Semi-supervised learning.
| Reference | AM process | Materials | Input | Output | ML method |
|---|---|---|---|---|---|
| Okaro et al. [250] | SLM | IN718 | PD | Faulty/acceptable | GMM |
| Larsen and Hooper [290] | SLM | - | HSI | Anamoly detection | VRNN |
| Li et al. [291] | LBAM | ASTM F75 I CoCrMo | Micrograph image | Defect detection | CNN |
| Yuan et al. [251] | SLM | SS316 | Video data | Track width and continuity | CNN |
| Yadav et al. [289] | SLM | AISilOMg | Optical tomography image | hot spot detection | A combination of KMC and KNN |
3.4. Reinforced learning
4. Challenges and opportunities
4.1. Challenges
4.1.1. Standardization
4.1.2. Data availability and model interpretation
4.1.3. Physics-informed Models
4.2. Opportunities
4.2.1. Moving toward PIML models
4.2.2. Employing different types of inputs
4.2.3. Developing practical models
4.2.4. Length scales
5. Summary and concluding thoughts
- •Supervised machine learning approaches are the most popular algorithms among ML algorithms in the defect detection and quality evaluation of 3D-printed parts. However, data preparation for supervised machine learning algorithms is costly and time-consuming, which may make these approaches less practical in the AM field.
- •Although unsupervised and semi-supervised techniques are more suitable for AM problems due to the cost of labeling data, these approaches have been less frequently used in the AM field.
- •Future work in the AM field is envisioned to move towards semi-supervised and reinforcement learning algorithms. Semi-supervised machine learning models have demonstrated a high potential to be widely used in the AM field, as they fully satisfy the issues with supervised and unsupervised machine learning algorithms.
- •Developing PIML models in the AM field is in its infancy, as the majority of the ML models developed to detect defects and evaluate the quality of 3D-printed parts ignored the physics of the problem and focused more on different methods of ML models and their accuracy.
- •Among these PIML models, PIMT, PIMC, PIMA, and PIMO models have been left underexplored in detecting defects and analyzing the quality of 3D-printed parts, while more attention needs to be paid to PIMI models. Investigating the benefits of simultaneously employing more than one of these methods is believed to be unexplored.
Declaration of Competing Interest
Acknowledgements
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